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import torch
from easydict import EasyDict
from lzero.policy import inverse_scalar_transform, select_action
import numpy as np
import random
from lzero.mcts.tree_search.mcts_ptree import EfficientZeroMCTSPtree as MCTSPtree
from lzero.mcts.tree_search.mcts_ctree import EfficientZeroMCTSCtree as MCTSCtree
import time
class MuZeroModelFake(torch.nn.Module):
"""
Overview:
Fake MuZero model just for test EfficientZeroMCTSPtree.
Interfaces:
__init__, initial_inference, recurrent_inference
"""
def __init__(self, action_num):
super().__init__()
self.action_num = action_num
def initial_inference(self, observation):
encoded_state = observation
batch_size = encoded_state.shape[0]
value = torch.zeros(size=(batch_size, 601))
value_prefix = [0. for _ in range(batch_size)]
policy_logits = torch.zeros(size=(batch_size, self.action_num))
latent_state = torch.zeros(size=(batch_size, 12, 3, 3))
reward_hidden_state_state = (torch.zeros(size=(1, batch_size, 16)), torch.zeros(size=(1, batch_size, 16)))
output = {
'searched_value': value,
'value_prefix': value_prefix,
'policy_logits': policy_logits,
'latent_state': latent_state,
'reward_hidden_state': reward_hidden_state_state
}
return EasyDict(output)
def recurrent_inference(self, hidden_states, reward_hidden_states, actions):
batch_size = hidden_states.shape[0]
latent_state = torch.zeros(size=(batch_size, 12, 3, 3))
reward_hidden_state_state = (torch.zeros(size=(1, batch_size, 16)), torch.zeros(size=(1, batch_size, 16)))
value = torch.zeros(size=(batch_size, 601))
value_prefix = torch.zeros(size=(batch_size, 601))
policy_logits = torch.zeros(size=(batch_size, self.action_num))
output = {
'searched_value': value,
'value_prefix': value_prefix,
'policy_logits': policy_logits,
'latent_state': latent_state,
'reward_hidden_state': reward_hidden_state_state
}
return EasyDict(output)
def ptree_func(policy_config, num_simulations):
"""
Overview:
Search on the tree of the Python implementation and record the time spent at different stages.
Arguments:
- policy_config: config of game.
- num_simulations: Number of simulations.
Returns:
- build_time: Type builds take time.
- prepare_time: time for prepare.
- search_time.
- total_time.
"""
batch_size = env_nums = policy_config.batch_size
action_space_size = policy_config.action_space_size
build_time = []
prepare_time = []
search_time = []
total_time = []
for n_s in num_simulations:
t0 = time.time()
model = MuZeroModelFake(action_num=action_space_size)
stack_obs = torch.zeros(
size=(
batch_size,
n_s,
), dtype=torch.float
)
policy_config.num_simulations = n_s
network_output = model.initial_inference(stack_obs.float())
latent_state_roots = network_output['latent_state']
reward_hidden_state_state = network_output['reward_hidden_state']
pred_values_pool = network_output['value']
value_prefix_pool = network_output['value_prefix']
policy_logits_pool = network_output['policy_logits']
# network output process
pred_values_pool = inverse_scalar_transform(pred_values_pool,
policy_config.model.support_scale).detach().cpu().numpy()
latent_state_roots = latent_state_roots.detach().cpu().numpy()
reward_hidden_state_state = (
reward_hidden_state_state[0].detach().cpu().numpy(), reward_hidden_state_state[1].detach().cpu().numpy()
)
policy_logits_pool = policy_logits_pool.detach().cpu().numpy().tolist()
action_mask = [[random.randint(0, 1) for _ in range(action_space_size)] for _ in range(env_nums)]
assert len(action_mask) == batch_size
assert len(action_mask[0]) == action_space_size
action_num = [int(np.array(action_mask[i]).sum()) for i in range(env_nums)]
legal_actions_list = [[i for i, x in enumerate(action_mask[j]) if x == 1] for j in range(env_nums)]
to_play = [np.random.randint(1, 3) for i in range(env_nums)]
assert len(to_play) == batch_size
# ============================================ptree=====================================#
for i in range(env_nums):
assert action_num[i] == len(legal_actions_list[i])
t1 = time.time()
roots = MCTSPtree.roots(env_nums, legal_actions_list)
build_time.append(time.time() - t1)
noises = [
np.random.dirichlet([policy_config.root_dirichlet_alpha] * int(sum(action_mask[j]))
).astype(np.float32).tolist() for j in range(env_nums)
]
t1 = time.time()
roots.prepare(policy_config.root_noise_weight, noises, value_prefix_pool, policy_logits_pool, to_play)
prepare_time.append(time.time() - t1)
t1 = time.time()
MCTSPtree(policy_config).search(roots, model, latent_state_roots, reward_hidden_state_state, to_play)
search_time.append(time.time() - t1)
total_time.append(time.time() - t0)
roots_distributions = roots.get_distributions()
roots_values = roots.get_values()
assert len(roots_values) == env_nums
assert len(roots_values) == env_nums
for i in range(env_nums):
assert len(roots_distributions[i]) == action_num[i]
temperature = [1 for _ in range(env_nums)]
for i in range(env_nums):
distributions = roots_distributions[i]
action_index, visit_count_distribution_entropy = select_action(
distributions, temperature=temperature[i], deterministic=False
)
action = np.where(np.array(action_mask[i]) == 1.0)[0][action_index]
assert action_index < action_num[i]
assert action == legal_actions_list[i][action_index]
print('\n action_index={}, legal_action={}, action={}'.format(action_index, legal_actions_list[i], action))
return build_time, prepare_time, search_time, total_time
def ctree_func(policy_config, num_simulations):
"""
Overview:
Search on the tree of the C++ implementation and record the time spent at different stages.
Arguments:
- policy_config: config of game.
- num_simulations: Number of simulations.
Returns:
- build_time: Type builds take time.
- prepare_time: time for prepare.
- search_time.
- total_time.
"""
batch_size = env_nums = policy_config.batch_size
action_space_size = policy_config.action_space_size
build_time = []
prepare_time = []
search_time = []
total_time = []
for n_s in num_simulations:
t0 = time.time()
model = MuZeroModelFake(action_num=action_space_size)
stack_obs = torch.zeros(
size=(
batch_size,
n_s,
), dtype=torch.float
)
policy_config.num_simulations = n_s
network_output = model.initial_inference(stack_obs.float())
latent_state_roots = network_output['latent_state']
reward_hidden_state_state = network_output['reward_hidden_state']
pred_values_pool = network_output['value']
value_prefix_pool = network_output['value_prefix']
policy_logits_pool = network_output['policy_logits']
# network output process
pred_values_pool = inverse_scalar_transform(pred_values_pool,
policy_config.model.support_scale).detach().cpu().numpy()
latent_state_roots = latent_state_roots.detach().cpu().numpy()
reward_hidden_state_state = (
reward_hidden_state_state[0].detach().cpu().numpy(), reward_hidden_state_state[1].detach().cpu().numpy()
)
policy_logits_pool = policy_logits_pool.detach().cpu().numpy().tolist()
action_mask = [[random.randint(0, 1) for _ in range(action_space_size)] for _ in range(env_nums)]
assert len(action_mask) == batch_size
assert len(action_mask[0]) == action_space_size
action_num = [int(np.array(action_mask[i]).sum()) for i in range(env_nums)]
legal_actions_list = [[i for i, x in enumerate(action_mask[j]) if x == 1] for j in range(env_nums)]
to_play = [np.random.randint(1, 3) for i in range(env_nums)]
assert len(to_play) == batch_size
# ============================================ctree=====================================#
for i in range(env_nums):
assert action_num[i] == len(legal_actions_list[i])
t1 = time.time()
roots = MCTSCtree.roots(env_nums, legal_actions_list)
build_time.append(time.time() - t1)
noises = [
np.random.dirichlet([policy_config.root_dirichlet_alpha] * int(sum(action_mask[j]))
).astype(np.float32).tolist() for j in range(env_nums)
]
t1 = time.time()
roots.prepare(policy_config.root_noise_weight, noises, value_prefix_pool, policy_logits_pool, to_play)
prepare_time.append(time.time() - t1)
t1 = time.time()
MCTSCtree(policy_config).search(roots, model, latent_state_roots, reward_hidden_state_state, to_play)
search_time.append(time.time() - t1)
total_time.append(time.time() - t0)
roots_distributions = roots.get_distributions()
roots_values = roots.get_values()
assert len(roots_values) == env_nums
assert len(roots_values) == env_nums
for i in range(env_nums):
assert len(roots_distributions[i]) == action_num[i]
temperature = [1 for _ in range(env_nums)]
for i in range(env_nums):
distributions = roots_distributions[i]
action_index, visit_count_distribution_entropy = select_action(
distributions, temperature=temperature[i], deterministic=False
)
action = np.where(np.array(action_mask[i]) == 1.0)[0][action_index]
assert action_index < action_num[i]
assert action == legal_actions_list[i][action_index]
print('\n action_index={}, legal_action={}, action={}'.format(action_index, legal_actions_list[i], action))
return build_time, prepare_time, search_time, total_time
def plot(ctree_time, ptree_time, iters, label):
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import pyplot
plt.style.use('seaborn-whitegrid')
palette = pyplot.get_cmap('Set1')
font1 = {
'family': 'Times New Roman',
'weight': 'normal',
'size': 18,
}
plt.figure(figsize=(20, 10))
# ctree
color = palette(0)
avg = np.mean(ctree_time, axis=0)
std = np.std(ctree_time, axis=0)
r1 = list(map(lambda x: x[0] - x[1], zip(avg, std)))
r2 = list(map(lambda x: x[0] + x[1], zip(avg, std)))
plt.plot(iters, avg, color=color, label="ctree", linewidth=3.0)
plt.fill_between(iters, r1, r2, color=color, alpha=0.2)
# ptree
ptree_time = np.array(ptree_time)
color = palette(1)
avg = np.mean(ptree_time, axis=0)
std = np.std(ptree_time, axis=0)
r1 = list(map(lambda x: x[0] - x[1], zip(avg, std)))
r2 = list(map(lambda x: x[0] + x[1], zip(avg, std)))
plt.plot(iters, avg, color=color, label="ptree", linewidth=3.0)
plt.fill_between(iters, r1, r2, color=color, alpha=0.2)
plt.legend(loc='lower right', prop=font1)
plt.title('{}'.format(label))
plt.xlabel('simulations', fontsize=22)
plt.ylabel('time', fontsize=22)
plt.savefig('{}-time.png'.format(label))
if __name__ == "__main__":
# cProfile.run("ctree_func()", filename="ctree_result.out", sort="cumulative")
# cProfile.run("ptree_func()", filename="ptree_result.out", sort="cumulative")
policy_config = EasyDict(
dict(
lstm_horizon_len=5,
model=dict(
support_scale=300,
categorical_distribution=True,
),
action_space_size=100,
num_simulations=100,
batch_size=512,
pb_c_base=1,
pb_c_init=1,
discount_factor=0.9,
root_dirichlet_alpha=0.3,
root_noise_weight=0.2,
dirichlet_alpha=0.3,
exploration_fraction=1,
device='cpu',
value_delta_max=0.01,
)
)
ACTION_SPCAE_SIZE = [16, 50]
BATCH_SIZE = [8, 64, 512]
NUM_SIMULATIONS = [i for i in range(20, 200, 20)]
# ACTION_SPCAE_SIZE = [50]
# BATCH_SIZE = [512]
# NUM_SIMULATIONS = [i for i in range(10, 50, 10)]
for action_space_size in ACTION_SPCAE_SIZE:
for batch_size in BATCH_SIZE:
policy_config.batch_size = batch_size
policy_config.action_space_size = action_space_size
ctree_build_time = []
ctree_prepare_time = []
ctree_search_time = []
ptree_build_time = []
ptree_prepare_time = []
ptree_search_time = []
ctree_total_time = []
ptree_total_time = []
num_simulations = NUM_SIMULATIONS
for i in range(3):
build_time, prepare_time, search_time, total_time = ctree_func(
policy_config, num_simulations=num_simulations
)
ctree_build_time.append(build_time)
ctree_prepare_time.append(prepare_time)
ctree_search_time.append(search_time)
ctree_total_time.append(total_time)
for i in range(3):
build_time, prepare_time, search_time, total_time = ptree_func(
policy_config, num_simulations=num_simulations
)
ptree_build_time.append(build_time)
ptree_prepare_time.append(prepare_time)
ptree_search_time.append(search_time)
ptree_total_time.append(total_time)
label = 'action_space_size_{}_batch_size_{}'.format(action_space_size, batch_size)
plot(ctree_build_time, ptree_build_time, iters=num_simulations, label=label + '_bulid_time')
plot(ctree_prepare_time, ptree_prepare_time, iters=num_simulations, label=label + '_prepare_time')
plot(ctree_search_time, ptree_search_time, iters=num_simulations, label=label + '_search_time')
plot(ctree_total_time, ptree_total_time, iters=num_simulations, label=label + '_total_time')
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